Autologging: Notebook

As part of the data science team, you want to try different modeling approaches during experimentation phase.To guarantee reproducibility, each approach has different parameters that you need to manually track. Vertex AI SDK for Python autologging, which is a one-line code SDK capability leveraging MLflow, provides automatic metrics and parameters tracking associated with your Vertex AI Experiments and experiment runs.

Notebook: Vertex AI Experiments Autologging

To see an example of autologging in Vertex AI Experiments, run the "Vertex AI Experiments: Autologging" Jupyter notebook in one of the following environments:

Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench user-managed notebooks | View on GitHub

In the "Vertex AI Experiments: Autologging" notebook, you'll learn how to use Vertex AI Experiments to:

• Enable autologging in the Vertex AI SDK for Python.
• Train scikit-learn model and see the resulting experiment run with metrics and parameters autologged to Vertex AI Experiments without setting an experiment run.
• Train TensorFlow model, check autologged metrics and parameters to Vertex AI Experiments by manually setting an experiment run with aiplatform.start_run() and aiplatform.end_run().
• Disable autologging in the Vertex AI SDK for Python, train a PyTorch model and check that none of the parameters or metrics are logged.

Relevant content

• Autolog data